Updated Relevant papers (markdown)

Relevant-papers.md

@@ -37,6 +37,14 @@ Modelling and Quantifying Membership Information Leakage in Machine Learning (20
 
 ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning (2020): https://arxiv.org/abs/2007.09339
 
+Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics (2020): https://arxiv.org/abs/2009.05669
+
+Quantifying Membership Privacy via Information Leakage (2020): https://arxiv.org/abs/2010.05965
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+Measuring Data Leakage in Machine-Learning Models with Fisher Information (2021): https://arxiv.org/abs/2102.11673
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+Bounding Information Leakage in Machine Learning (2021): https://arxiv.org/pdf/2105.03875v1.pdf
+
 ## Differential privacy for ML models:
 Deep Learning with Differential Privacy (2016): https://arxiv.org/abs/1607.00133